AI Enterprise Architect

About the Role
We are seeking an experienced AI Enterprise Architect who can bridge business requirements, enterprise technology, software engineering, and emerging AI capabilities.
The ideal candidate has experience in both startup and enterprise environments and understands the balance between rapid execution and scalable architecture. This role requires the ability to engage with CIOs, CTOs, and executive stakeholders while also working closely with engineering teams on architecture, integrations, APIs, and implementation.
This is not a purely advisory or documentation-focused role. We are looking for someone who can move seamlessly from strategy → architecture → execution.
Key Responsibilities
Solution Architecture
• Translate business requirements into scalable technical solutions.

• Design enterprise application, platform, integration, and AI architectures.

• Define application, data, security, infrastructure, and integration architectures.

• Create architecture blueprints, specifications, and implementation roadmaps.

• Evaluate build vs. buy vs. integrate decisions.

• Identify technical risks, dependencies, and trade-offs.

• Ensure solutions are secure, scalable, maintainable, and commercially viable.
Enterprise Architecture & Client Engagement
• Partner directly with CTOs, CIOs, Heads of Engineering, and Digital Leaders.

• Lead discovery workshops and architecture sessions.

• Understand enterprise technology landscapes and integration opportunities.

• Convert business objectives into actionable technology strategies.

• Present architecture recommendations to technical and business stakeholders.

• Support enterprise proposals, POCs, RFPs, and technical evaluations.
AI & Emerging Technology
• Architect solutions using LLMs, Generative AI, and AI Agents.

• Design RAG platforms, knowledge systems, and enterprise AI assistants.

• Evaluate AI models based on performance, cost, privacy, and latency.

• Design AI orchestration and multi-agent solutions.

• Implement vector search, embeddings, and semantic retrieval architectures.

• Integrate AI solutions with enterprise platforms and APIs.

• Implement evaluation frameworks, observability, guardrails, and risk controls.

• Stay current with rapidly evolving AI technologies and architecture patterns.
Startup Environment
We value architects who can:
Move Fast • Operate with Ambiguity • Build MVPs • Make Pragmatic Decisions • Work Directly with Founders • Wear Multiple Hats • Balance Speed and Scalability
What Success Looks Like
Within the first 90-180 days, you will:
• Establish architecture standards and reusable patterns.

• Lead enterprise architecture discussions and workshops.

• Accelerate solution design and POC delivery.

• Improve enterprise technical credibility.

• Reduce technical debt while maintaining startup velocity.

• Help productize repeatable enterprise solutions.
Required Experience
• 8+ years of software engineering and architecture experience.
• Experience as a Solution Architect, Enterprise Architect, or Technical Architect.
• Proven track record delivering enterprise-grade software solutions.
• Strong cloud architecture and distributed systems expertise.
• Experience with APIs, integrations, microservices, and event-driven architectures.
• Strong understanding of scalability, reliability, security, and performance.
• Real-world experience architecting AI-powered solutions.
• Experience working directly with enterprise customers and stakeholders.
• Startup or high-growth technology company experience preferred.
AI Experience (Must Have)
AI Capabilities
LLM Applications • Generative AI • AI Agents • Agentic AI • RAG • Vector Databases • Embeddings • LLM APIs • AI Orchestration • Model Evaluation • AI Guardrails • AI Observability • Prompt Engineering • Enterprise AI Architecture • Workflow Automation • Multi-Agent Systems • Fine-Tuning • AI Security & Privacy
Relevant Technologies
OpenAI • Anthropic • Gemini • LangChain • LlamaIndex • Azure OpenAI • AWS Bedrock • Vertex AI • Pinecone • Weaviate • Milvus
Preferred Technical Background
Architecture
Microservices • Distributed Systems • Event-Driven Architecture • Serverless • Domain-Driven Design • API-First Architecture
Cloud
AWS • Azure • GCP • Kubernetes • Docker • Terraform
Backend
Python • Java • Node.js • TypeScript • .NET • REST • GraphQL • Messaging Systems
Data
PostgreSQL • MySQL • NoSQL • Redis • Data Warehouses • Vector Databases • Data Pipelines
AI
LLM APIs • RAG • AI Agents • Embeddings • Vector Search • AI Orchestration • Model Evaluation • AI Observability
Business & Leadership Skills
• Understand business problems before proposing technology.
• Communicate architecture clearly to technical and non-technical audiences.
• Make ROI-driven decisions.
• Balance speed, cost, scalability, and technical debt.
• Influence engineering teams and executive stakeholders.
• Mentor engineers and architects.
• Take ownership of technical outcomes.
Key Performance Indicators (KPIs)
Business
Enterprise POC Conversion Rate • Technical Contribution to Closed Deals • Solution Design Turnaround Time • POC Success Rate • Customer Technical Satisfaction
Technology
Architecture Quality • Scalability • Reliability • Security Readiness • Reusability • Technical Debt Reduction
AI
Production AI Solutions Delivered • AI Accuracy • AI Cost Optimization • AI Velocity • Reusable AI Components
Execution
Architecture-to-Implementation Cycle Time • Engineering Adoption of Standards • Successful Roadmap Delivery • Reduced Architecture Delivery Blockers